{"id":3854523,"url":"https://alion.io/job/cake-by-vpbank-data-scientist","title":"Data Scientist","company":{"id":49440,"name":"Cake by VPBank","domain":"cake.vn","url":"https://alion.io/company/cake-by-vpbank","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":86,"open_postings":26,"ghost_share":0.154,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":84,"computed_at":"2026-11-09T06:03:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ho Chi Minh City, Vietnam"],"countries":["VN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"Scikit-learn","optional":true}],"status":"live","first_seen_at":"2026-11-06T03:58:09Z","employer_posted_date":null,"last_verified_at":"2026-11-06T03:58:09Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"About the Role\nCake is a Vietnamese digital bank with over 6 million users, building toward becoming a NextGen AI Bank, where data and AI are at the core of every product and decision. \nIn this role you'll be embedded in the Lending team as their data partner. You'll dig into the data to help the team plan and solve problems, propose where machine learning can help, and drive those solutions to delivery. You'll learn predictive modeling on the job, with hands-on guidance from our senior data scientists.\nWhat You'll Do\nAnalyze deeply, then propose the right solution. Work with the Lending team to understand their goals and pain points. Run deep-dive analyses (funnel and conversion, customer behavior, portfolio and risk signals, product performance) to support their planning and problem solving. Turn the findings into clear recommendations, and where it makes sense, propose a suitable ML solution such as credit scoring, propensity, or recommendation.\nBuild and improve models, with guidance. Develop, evaluate, and improve predictive models and segmentation alongside senior data scientists. You'll learn feature engineering, validation, and monitoring on real lending problems, and keep models interpretable for non-technical stakeholders.\nDrive delivery. Own projects from problem definition to delivery. Coordinate across Lending, Risk, Product, Data Engineering, and ML Engineering to align priorities, manage timelines, unblock dependencies, and make sure solutions reach production and create business impact.\nYou'll also share knowledge with the team and grow along a clear path from Data Analyst to Data Scientist to Senior DS / AI roles.\n\nYêu cầu công việc\nWhat We're Looking For\nMust-have\nBackground\nBachelor's degree in Statistics, Mathematics, Data Science, Computer Science, Economics, or a related field.\n3+ years in data analytics or a similar role, preferably in fintech, banking, lending, e-commerce, or tech.\nData-driven and problem-solving mindset\nTurns vague business questions into structured, testable problems and knows when an answer is good enough to act on.\nIs rigorous with data: checks quality, questions surprising results, and is honest about uncertainty.\nAnalytical and technical foundation\nStrong in SQL, Python (pandas, numpy, visualization), and statistics, with basic knowledge of how predictive models are built and evaluated (AUC, precision/recall).\nHas a track record of deep-dive analyses that changed a decision or plan, beyond reports and dashboards.\nBusiness acumen\nUnderstands how a digital business makes money and manages risk, and connects analysis to metrics like conversion, approval rate, and default rate.\nPrioritizes problems by business value and can say when a model isn't the right answer.\nData storytelling\nTurns analysis into a clear narrative that leads with the insight and ends with a concrete recommendation.\nAdapts the message to the audience, from product teams to senior leadership, and explains models so non-technical stakeholders trust and act on them.\nGetting things done\nOwns work from problem definition through delivery, with strong project management and a bias for action.\nMoves work across teams by aligning priorities and unblocking dependencies without relying on formal authority.\n\nNice-to-have (we'll train you)\nHands-on machine learning experience (scikit-learn or similar), including any predictive or scoring project.\nExperience in lending, credit risk, or customer analytics.\nExperience with big data tools or cloud platforms (AWS, GCP, Azure).","description_format":"text","description_chars":3537,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Digital Banks & Neobanks"],"lifecycle":[{"event":"open","at":"2026-11-06T05:26:35Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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